Better resolution when referencing to concepts

ABSTRACT

Systems and processes for operating a virtual assistant programmed to refer to shared domain concepts using concept nodes are provided. In an example process, user speech input is received. A textual representation of the user speech input is generated. The textual representation is parsed to determine a primary domain representing a user intent for the textual representation. A first substring from the textual representation that corresponds to a first attribute of the primary domain is identified. The identified first substring is parsed to determine a secondary domain representing a user intent for the first substring. A task flow comprising one or more tasks is performed based on the primary domain and the secondary domain.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No. 14/500,703, filed on Sep. 29, 2014, entitled BETTER RESOLUTION WHEN REFERENCING TO CONCEPTS, which claims priority from U.S. Provisional Ser. No. 62/006,036, filed on May 30, 2014, entitled BETTER RESOLUTION WHEN REFERENCING TO CONCEPTS, which are both hereby incorporated by reference in their entirety for all purposes.

FIELD

This relates generally to natural language processing and, more specifically, to modeling domains to refer to cross-domain concepts.

BACKGROUND

Intelligent automated assistants (or virtual assistants) provide an intuitive interface between users and electronic devices. These assistants can allow users to interact with devices or systems using natural language in spoken and/or text forms. For example, a user can access the services of an electronic device by providing a spoken user input in natural language form to a virtual assistant associated with the electronic device. The virtual assistant can perform natural language processing on the spoken user input to infer the user's intent and operationalize the user's intent into tasks. The tasks can then be performed by executing one or more functions of the electronic device, and a relevant output can be returned to the user in natural language form.

Some virtual assistants can be implemented using active ontologies to simplify the software engineering and data maintenance of the virtual assistant systems. Active ontologies can represent an integration of data modeling and execution environments for assistants and can provide a framework to tie together the various sources of models and data (e.g., domain concepts, task flows, vocabulary, language pattern recognizers, dialog context, user personal information, mappings from domain and task requests to external services, and the like). Implementing a virtual assistant in this way allows the virtual assistant to quickly and accurately respond to a user input in natural language form. However, current active ontology architectures can make it difficult to add new knowledge domains representing a subject, genre, area of interest, group of similar requests, or the like, to the active ontology.

SUMMARY

Systems and processes for operating a virtual assistant are provided. One example process can include receiving a textual representation of user speech and determining a primary user intent for the textual representation of user speech. The process can further include identifying a first type of concept referred to by the primary user intent, identifying a first substring from the textual representation of user speech corresponding to the first type of concept, and determining a secondary user intent for the first substring. The process can further include performing a task flow comprising one or more tasks based at least in part on the primary user intent for the textual representation of user speech and the secondary user intent for the first substring.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an exemplary environment in which a virtual assistant can operate according to various examples.

FIG. 2 illustrates an exemplary user device according to various examples.

FIG. 3 illustrates a block diagram of an exemplary virtual assistant according to various examples.

FIG. 4 illustrates a portion of an exemplary active ontology according to various examples.

FIG. 5 illustrates a portion of another exemplary active ontology according to various examples.

FIG. 6 illustrates an exemplary process for operating a virtual assistant according to various examples.

FIG. 7 illustrates a functional block diagram of an electronic device configured to operate a virtual assistant according to various examples.

DETAILED DESCRIPTION

In the following description of examples, reference is made to the accompanying drawings in which it is shown by way of illustration specific examples that can be practiced. It is to be understood that other examples can be used and structural changes can be made without departing from the scope of the various examples.

This relates to systems and processes for operating a virtual assistant programmed to refer to shared domain concepts using concept nodes. A concept node can represent a particular type of concept, such as a person, place, time, event, or the like, and can be used within a domain of an active ontology to refer to a concept without having to identify all possible sources for that concept, and without associated logic to select the appropriate knowledge representation of user intent from the various domains for a given user input. In some examples, to process a textual representation of user speech using an active ontology having these concept nodes, a primary user intent can be determined from the textual representation of user speech. Concepts referred to by the primary user intent can be identified, and substrings of the textual representation of user speech corresponding to the concepts can be identified. Secondary user intents for the substrings can be determined and a task flow based on the primary user intent and the secondary user intents can be generated and performed.

System Overview

FIG. 1 illustrates exemplary system 100 for implementing a virtual assistant according to various examples. The terms “virtual assistant,” “digital assistant,” “intelligent automated assistant,” or “automatic digital assistant” can refer to any information processing system that interprets natural language input in spoken and/or textual form to infer user intent, and performs actions based on the inferred user intent. For example, to act on an inferred user intent, the system can perform one or more of the following: identifying a task flow with steps and parameters designed to accomplish the inferred user intent; inputting specific requirements from the inferred user intent into the task flow; executing the task flow by invoking programs, methods, services, APIs, or the like; and generating output responses to the user in an audible (e.g., speech) and/or visual form.

A virtual assistant can be capable of accepting a user request at least partially in the form of a natural language command, request, statement, narrative, and/or inquiry. Typically, the user request seeks either an informational answer or performance of a task by the virtual assistant. A satisfactory response to the user request can include provision of the requested informational answer, performance of the requested task, or a combination of the two. For example, a user can ask the virtual assistant a question, such as “Where am I right now?” Based on the user's current location, the virtual assistant can answer, “You are in Central Park.” The user can also request the performance of a task, for example, “Please remind me to call Mom at 4 p.m. today.” In response, the virtual assistant can acknowledge the request and then create an appropriate reminder item in the user's electronic schedule. During the performance of a requested task, the virtual assistant can sometimes interact with the user in a continuous dialogue involving multiple exchanges of information over an extended period of time. There are numerous other ways of interacting with a virtual assistant to request information or performance of various tasks. In addition to providing verbal responses and taking programmed actions, the virtual assistant can also provide responses in other visual or audio forms (e.g., as text, alerts, music, videos, animations, etc.).

An example of a virtual assistant is described in Applicants' U.S. Utility application Ser. No. 12/987,982 for “Intelligent Automated Assistant,” filed Jan. 10, 2011, the entire disclosure of which is incorporated herein by reference.

As shown in FIG. 1, in some examples, a virtual assistant can be implemented according to a client-server model. The virtual assistant can include a client-side portion executed on a user device 102, and a server-side portion executed on a server system 110. User device 102 can include any electronic device, such as a mobile phone, tablet computer, portable media player, desktop computer, laptop computer, PDA, television, television set-top box, wearable electronic device, or the like, and can communicate with server system 110 through one or more networks 108, which can include the Internet, an intranet, or any other wired or wireless public or private network. The client-side portion executed on user device 102 can provide client-side functionalities, such as user-facing input and output processing and communications with server system 110. Server system 110 can provide server-side functionalities for any number of clients residing on a respective user device 102.

Server system 110 can include one or more virtual assistant servers 114 that can include a client-facing I/O interface 122, one or more processing modules 118, data and model storage 120, and an I/O interface to external services 116. The client-facing I/O interface 122 can facilitate the client-facing input and output processing for virtual assistant server 114. The one or more processing modules 118 can utilize data and model storage 120 to determine the user's intent based on natural language input, and perform task execution based on inferred user intent. In some examples, virtual assistant server 114 can communicate with external services 124, such as telephony services, calendar services, information services, messaging services, navigation services, and the like, through network(s) 108 for task completion or information acquisition. The I/O interface to external services 116 can facilitate such communications.

Server system 110 can be implemented on one or more standalone data processing devices or a distributed network of computers. In some examples, server system 110 can employ various virtual devices and/or services of third party service providers (e.g., third-party cloud service providers) to provide the underlying computing resources and/or infrastructure resources of server system 110.

Although the functionality of the virtual assistant is shown in FIG. 1 as including both a client-side portion and a server-side portion, in some examples, the functions of the assistant can be implemented as a standalone application installed on a user device. In addition, the division of functionalities between the client and server portions of the virtual assistant can vary in different examples. For instance, in some examples, the client executed on user device 102 can be a thin-client that provides only user-facing input and output processing functions, and delegates all other functionalities of the virtual assistant to a backend server.

User Device

FIG. 2 is a block diagram of a user-device 102 according to various examples. As shown, user device 102 can include a memory interface 202, one or more processors 204, and a peripherals interface 206. The various components in user device 102 can be coupled together by one or more communication buses or signal lines. User device 102 can further include various sensors, subsystems, and peripheral devices that are coupled to the peripherals interface 206. The sensors, subsystems, and peripheral devices gather information and/or facilitate various functionalities of user device 102.

For example, user device 102 can include a motion sensor 210, a light sensor 212, and a proximity sensor 214 coupled to peripherals interface 206 to facilitate orientation, light, and proximity sensing functions. One or more other sensors 216, such as a positioning system (e.g., a GPS receiver), a temperature sensor, a biometric sensor, a gyroscope, a compass, an accelerometer, and the like, are also connected to peripherals interface 206, to facilitate related functionalities.

In some examples, a camera subsystem 220 and an optical sensor 222 can be utilized to facilitate camera functions, such as taking photographs and recording video clips. Communication functions can be facilitated through one or more wired and/or wireless communication subsystems 224, which can include various communication ports, radio frequency receivers and transmitters, and/or optical (e.g., infrared) receivers and transmitters. An audio subsystem 226 can be coupled to speakers 228 and a microphone 230 to facilitate voice-enabled functions, such as voice recognition, voice replication, digital recording, and telephony functions.

In some examples, user device 102 can further include an I/O subsystem 240 coupled to peripherals interface 206. I/O subsystem 240 can include a touch screen controller 242 and/or other input controller(s) 244. Touch-screen controller 242 can be coupled to a touch screen 246. Touch screen 246 and the touch screen controller 242 can, for example, detect contact and movement or break thereof using any of a plurality of touch sensitivity technologies, such as capacitive, resistive, infrared, and surface acoustic wave technologies, proximity sensor arrays, and the like. Other input controller(s) 244 can be coupled to other input/control devices 248, such as one or more buttons, rocker switches, a thumb-wheel, an infrared port, a USB port, and/or a pointer device such as a stylus.

In some examples, user device 102 can further include a memory interface 202 coupled to memory 250. Memory 250 can include any electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, a portable computer diskette (magnetic), a random access memory (RAM) (magnetic), a read-only memory (ROM) (magnetic), an erasable programmable read-only memory (EPROM) (magnetic), a portable optical disc such as CD, CD-R, CD-RW, DVD, DVD-R, or DVD-RW, or flash memory such as compact flash cards, secured digital cards, USB memory devices, memory sticks, and the like. In some examples, a non-transitory computer-readable storage medium of memory 250 can be used to store instructions (e.g., for performing some or all of process 600, described below) for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device, and execute the instructions. In other examples, the instructions (e.g., for performing process 600, described below) can be stored on a non-transitory computer-readable storage medium of server system 110, or can be divided between the non-transitory computer-readable storage medium of memory 250 and the non-transitory computer-readable storage medium of server system 110. In the context of this document, a “non-transitory computer readable storage medium” can be any medium that can contain or store the program for use by or in connection with the instruction execution system, apparatus, or device.

In some examples, the memory 250 can store an operating system 252, a communication module 254, a graphical user interface module 256, a sensor processing module 258, a phone module 260, and applications 262. Operating system 252 can include instructions for handling basic system services and for performing hardware dependent tasks. Communication module 254 can facilitate communicating with one or more additional devices, one or more computers, and/or one or more servers. Graphical user interface module 256 can facilitate graphic user interface processing. Sensor processing module 258 can facilitate sensor related processing and functions. Phone module 260 can facilitate phone-related processes and functions. Application module 262 can facilitate various functionalities of user applications, such as electronic-messaging, web browsing, media processing, navigation, imaging, and/or other processes and functions.

As described herein, memory 250 can also store client-side virtual assistant instructions (e.g., in a virtual assistant client module 264) and various user data 266 (e.g., user-specific vocabulary data, preference data, and/or other data, such as the user's electronic address book, to-do lists, shopping lists, etc.) to provide the client-side functionalities of the virtual assistant.

In various examples, virtual assistant client module 264 can be capable of accepting voice input (e.g., speech input), text input, touch input, and/or gestural input through various user interfaces (e.g., I/O subsystem 240, audio subsystem 226, or the like) of user device 102. Virtual assistant client module 264 can also be capable of providing output in audio (e.g., speech output), visual, and/or tactile forms. For example, output can be provided as voice, sound, alerts, text messages, menus, graphics, videos, animations, vibrations, and/or combinations of two or more of the above. During operation, virtual assistant client module 264 can communicate with the virtual assistant server using communication subsystem 224.

In some examples, virtual assistant client module 264 can utilize the various sensors, subsystems, and peripheral devices to gather additional information from the surrounding environment of user device 102 to establish a context associated with a user, the current user interaction, and/or the current user input. In some examples, virtual assistant client module 264 can provide the contextual information or a subset thereof with the user input to the virtual assistant server to help infer the user's intent. The virtual assistant can also use the contextual information to determine how to prepare and deliver outputs to the user.

In some examples, the contextual information that accompanies the user input can include sensor information, such as lighting, ambient noise, ambient temperature, images or videos of the surrounding environment, distance to another object, and the like. The contextual information can further include information associated with the physical state of user device 102 (e.g., device orientation, device location, device temperature, power level, speed, acceleration, motion patterns, cellular signal strength, etc.) or the software state of user device 102 (e.g., running processes, installed programs, past and present network activities, background services, error logs, resources usage, etc.). Any of these types of contextual information can be provided to the virtual assistant server 114 as contextual information associated with a user input.

In some examples, virtual assistant client module 264 can selectively provide information (e.g., user data 266) stored on user device 102 in response to requests from the virtual assistant server 114. Virtual assistant client module 264 can also elicit additional input from the user via a natural language dialogue or other user interfaces upon request by virtual assistant server 114. Virtual assistant client module 264 can pass the additional input to virtual assistant server 114 to help virtual assistant server 114 in intent inference and/or fulfillment of the user's intent expressed in the user request.

In various examples, memory 250 can include additional instructions or fewer instructions. Furthermore, various functions of user device 102 can be implemented in hardware and/or in firmware, including in one or more signal processing and/or application specific integrated circuits.

Conceptual Architecture

FIG. 3 illustrates a simplified block diagram of an example virtual assistant 300 that can be implemented using system 100. Virtual assistant 300 can receive user input 304 in the form of an audio or textual representation of the user's natural language input and, optionally, contextual information 306 to generate an output response 308 to the user in audio or text form, as well as other actions 310 (e.g., sending an email, setting an alarm, etc.) performed in response to the user input. Virtual assistant 300 can include multiple different types of components, devices, modules, processes, systems, and the like, which, for example, may be implemented and/or instantiated via the use of hardware and/or combinations of hardware and software. For example, as shown in FIG. 3, virtual assistant 300 can include one or more of the following types of systems, components, devices, processes, and the like (or combinations thereof): one or more active ontologies 350; active input elicitation component(s) 394; short term personal memory component(s) 352; long-term personal memory component(s) 354; domain models component(s) 356; vocabulary component(s) 358; language pattern recognizer(s) component(s) 360; language interpreter component(s) 370; domain entity database(s) 372; dialog flow processor component(s) 380; services orchestration component(s) 382; services component(s) 384; task flow models component(s) 386; dialog flow models component(s) 387; service models component(s) 388; and output processor component(s) 390. A detailed description of these components can be found in Applicants' U.S. Utility application Ser. No. 12/987,982 for “Intelligent Automated Assistant,” filed Jan. 10, 2011.

Active Ontology

As mentioned above, active ontology 350 can represent an integration of data modeling and execution environments for assistants and can provide a framework to tie together the various sources of models and data (e.g., domain concepts, task flows, vocabulary, language pattern recognizers, dialog context, user personal information, mappings from domain and task requests to external services, and the like). FIG. 4 illustrates a portion of a typical active ontology 400 that can be used as active ontology 350 in virtual assistant 300. In particular, the displayed portion of active ontology 400 illustrates the way that concepts from different domains can be tied together in a typical active ontology.

As shown in FIG. 4, active ontology 400 includes a weather domain 402 having a weather node 404. Weather node 404 is connected to time node 406 and place node 414, indicating that weather has a concept of both time and place (e.g., weather near the hockey game Friday night). In typical active ontologies like that shown in FIG. 4, domains that refer to a concept, such as place, time, person, event, or the like, can be programmed to specifically refer to every other domain within the active ontology that can produce that concept. These domains can also be programmed to include logic for calling the referenced domains, receiving the knowledge representations of user intent generated by those domains, identifying the knowledge representations of user intent that is most likely to provide the appropriate concept, and extracting the relevant concept from that knowledge representation of user intent.

To illustrate, time node 406 is shown as being connected to logic 408, which is connected to sports domain 418, reminder domain 420, business domain 422, and email domain 424. This configuration indicates that the time concept of weather node 404 can be produced by sports domain 418, reminder domain 420, business domain 422, or email domain 424. For example, the time concept of weather node 404 can be referenced with respect to information contained in sports domain 418 (e.g., weather during a sporting event), information contained in reminder domain 420 (e.g., weather at a time of a reminder set by the user), information contained in business domain 422 (e.g., weather when a particular business is set to open), or information contained in email domain 424 (e.g., weather when an email was received). Logic 408 can be used to identify the knowledge representation of user intent produced by sports domain 418, reminder domain 420, business domain 422, or email domain 424 that is most likely to provide the appropriate time concept and to extract the time concept from that knowledge representation of user intent.

Similarly, place node 414 is shown as being connected to logic 416, which is connected to sports domain 418, reminder domain 420, and business domain 422. This configuration indicates that the place concept of weather node 404 can be produced by sports domain 418, reminder domain 420, or business domain 422. For example, the place concept of weather node 404 can be referenced with respect to information contained in sports domain 418 (e.g., weather at a sports stadium), information contained in reminder domain 420 (e.g., weather at a location associated with a reminder set by the user), or information contained in business domain 422 (e.g., weather at the headquarters of a business). Logic 416 can be used to identify the knowledge representation of user intent produced by sports domain 418, reminder domain 420, or business domain 422 that is most likely to provide the appropriate place concept and to extract the place concept from that knowledge representation of user intent.

A domain that is configured in a manner similar to that of weather domain 402, shown in FIG. 4, can be used to process user inputs that reference concepts with respect to information contained in the specific domains that the domain is programmed to reference. However, it can be problematic to add new domains to this type of active ontology because any concepts referred to by the new domain must be programmed to refer to every other existing domain within the active ontology that can produce those concepts. Additionally, the existing domains within the active ontology would need to be modified to refer to any concepts produced by the new domain. For example, to add a restaurant domain that both refers to and produces a time and place concept, the restaurant domain would have to be programmed to specifically refer to weather domain 402, sports domain 418, reminder domain 420, business domain 422, and email domain 424 for the time concept, and to refer to weather domain 402, sports domain 418, reminder domain 420, and business domain 422 for the place concept. The restaurant domain would also need to be programmed with logic for calling the referenced domains, receiving the knowledge representations of user intent generated by those domains, identifying the knowledge representations of user intent that are most likely to provide the appropriate concepts, and extracting the relevant concepts from those knowledge representations of user intent. The logic of the existing weather domain 402, sports domain 418, reminder domain 420, business domain 422, and email domain 424 would have to similarly be modified. Thus, referencing concepts between domains in this way may be overly burdensome for active ontologies having numerous domains.

While not shown, it should be appreciated that weather domain 402 can further include other types of weather-related information, such as vocabulary, entities, other concepts, properties, task flows that can be performed, dialog flows that can be performed, services that can be invoked, relationships between any of the forgoing, or the like. The other domains of active ontology 400 can similarly include any type of information related to their respective subjects, genres, areas of interest, groups of similar requests, or the like.

Concept Node

FIG. 5 illustrates a portion of an active ontology 500 that is similar to active ontology 400, but that includes “concept nodes” (e.g., time concept node 502 and place concept node 506) for referring to concepts within its various domains. A concept node can represent a particular type of concept, such as a person, place, time, event, or the like, and can be used within a domain of an active ontology to refer to a concept without having to identify all possible sources for that concept, and without associated logic to select the appropriate knowledge representation of user intent from the various domains for a given user input.

A concept node can include a non-terminal node having a single terminal node and a semantic tag that defines the type of concept that the concept node supports (e.g., person, place, time, event, or the like). The terminal node can be configured to identify portions or substrings of a user input that likely refer to the concept type of concept node. In some examples, the terminal node can identify substrings of a user input that likely refer to a concept type by performing a variable match process using one or more seed words that are likely associated with the type of concept. For example, a terminal node for a place concept node can include the seed word “near,” indicating that the portion of an utterance following the word “near” likely refers to a place concept. Similarly, a terminal node for a time concept node can include the seed word “during,” indicating that the portion of an utterance following the word “during” likely refers to a time concept. When used to process the user input “what's the weather like near my brother's house during the Super Bowl?”, the terminal node for the place concept node can be used to identify “my brother's house” as a substring of the user input that likely refers to a place concept, and the terminal node for the time concept node can be used to identify “the Super Bowl” as a substring of the user input that likely refers to a time concept. It should be appreciated that identifying a substring of a user input that likely refers to a concept type can include identifying multiple potential substrings of the user input that could refer to the concept and selecting the potential substring having the highest confidence score as the substring of the user input that likely refers to the concept type.

While specific algorithms and seed words are provided above for the terminal nodes of a time concept node and a place concept node, it should be appreciated that terminal nodes having other algorithms and/or seed words for identifying substrings of a user input that correspond to a particular concept type can be used. Additionally, other types of concept nodes can be created and used in the domains of an active ontology by creating appropriate semantic tags for the concept nodes and defining their terminal nodes in such a way so as to identify substrings of an utterance that likely correspond to their respective concept types.

To illustrate the use of a concept node in an active ontology, FIG. 5 shows active ontology 500 that, similar to active ontology 400, includes sports domain 418, reminder domain 420, business domain 422, email domain 424, and weather domain 402 having weather node 404. However, in contrast to active ontology 400, weather node 404 in FIG. 5 can instead be connected to time concept node 502 and place concept node 506 rather than time node 406 and place node 414. Additionally, unlike time node 406 and place node 414 in FIG. 4, time concept node 502 and place concept node 506 need not refer to every other domain within active ontology 500 that can produce those concepts and do not require logic for selecting the appropriate knowledge representation of user intent from the various domains for a given user input. Instead, time concept node 502 and place concept node 506 can include terminal node 504 and terminal node 508, respectively, which can be used to identify substrings of a user input that likely refer to the time and place concepts, respectively.

While only two concept nodes are shown, it should be appreciated that the domains of active ontology 500 can include any number and type of concept nodes, and any elements or nodes within those domains that refer to a concept can be connected to the appropriate concept node. Configuring active ontology 500 in this way simplifies the process of adding new domains to the active ontology. For example, to add a restaurant domain that both refers to and produces a time and place concept, the restaurant domain can simply be programmed to include a time concept node similar or identical to time concept node 502 and a place concept node similar or identical to place concept node 506. Unlike adding the restaurant domain to active ontology 400, the newly created restaurant domain does not need to be programmed to refer to weather domain 402, sports domain 418, reminder domain 420, business domain 422, and email domain 424 for the time concept, and does not need to be programmed to refer to weather domain 402, sports domain 418, reminder domain 420, and business domain 422 for the place concept. Additionally, the existing weather domain 402, sports domain 418, reminder domain 420, business domain 422, and email domain 424 in active ontology 500 do not need to be modified to refer to the newly added restaurant domain.

Process for Operating a Virtual Assistant

FIG. 6 illustrates an exemplary process 600 for operating a virtual assistant programmed to refer to concepts using concept nodes according to various examples. In some examples, process 600 can be performed using a system similar or identical to system 100 and that utilizes an active ontology having concept nodes similar or identical to those shown in FIG. 5.

At block 602, an audio input including user speech can be received at a user device. In some examples, a user device (e.g., user device 102) can receive audio input that includes a user's speech via a microphone (e.g., microphone 230). The microphone can convert the audio input into an analog or digital representation, and provide the audio data to one or more processors (e.g., processor(s) 204).

At block 604, the user speech of the audio input can be converted into a textual representation of the user speech. The user speech can be converted using any known speech-to-text conversion process. In some examples, the user speech can be converted into the textual representation locally on the user device. In other examples, the user device can transmit data corresponding to the audio input to a remote server (e.g., server system 110) capable of performing the speech-to-text conversion process.

A multi-pass natural language process represented by blocks 606, 608, 610, and 612 can be performed on the textual representation of user speech. Specifically, at block 606, the textual representation of user speech can be received or accessed, and a first pass of the multi-pass natural language process can be performed to determine a primary user intent from the textual representation of user speech. As discussed in greater detail in Applicants' U.S. Utility application Ser. No. 12/987,982 for “Intelligent Automated Assistant,” filed Jan. 10, 2011, determining user intent can include analyzing, by processing modules 118 using the various components of virtual assistant 300 shown in FIG. 3, the textual representation of user speech to identify possible parse results or interpretations for the textual representation of user speech. Generally, the parse results can include associations of data in the user input with concepts, relationships, properties, instances, and/or other nodes and/or data structures in models, databases, and/or other representations of user intent and context. The parse results can include syntactic parse results that associate data in the user input with structures that represent syntactic parts of speech, clauses, and phrases including multiword names, sentence structure, and/or other grammatical graph structures. The parse results can also include semantic parse results that associate data in the user input with structures that represent concepts, relationships, properties, entities, quantities, propositions, and/or other representations of meaning and user intent. Determining user intent can further include determining a confidence score for each of the alternative parse results (e.g., syntactic or semantic parse results) representing the likelihood that a particular parse result is the correct parse result to apply to the textual representation of user speech. The primary user intent can be determined based on the knowledge representation of user intent associated with the parse result having the highest confidence score. For example, the primary user intent can be determined to include the nodes associated with vocabulary, entities, concepts, properties, task flows that can be performed, dialog flows that can be performed, services that can be invoked, relationships between any of the forgoing, or the like, of the knowledge representation of user intent associated with the parse result having the highest confidence score.

At block 608, a first type of concept referred to by the primary user intent determined at block 606 can be identified. In some examples, identifying the first type of concept referred to by the primary user intent can include searching the primary user intent structure for concept nodes similar or identical to those described above with respect to FIG. 5. For example, the primary user intent structure can be searched for a time concept node, place concept node, person concept node, event concept node, or the like. If a concept node is found within the primary user intent structure, the semantic tag that defines the type of concept associated with the concept node can be read to identify and store the first type of concept referred to by the primary user intent.

It should be appreciated that the primary user intent determined at block 606 can include any number and type of concept nodes. In these examples, block 608 can further include identifying those other types of concept nodes, reading their associated semantic tags, and recording the concept types indicated by the semantic tags.

At block 610, a first substring from the textual representation of user speech that corresponds to the first type of concept identified at block 608 can be identified. In some examples, identifying the first substring can include performing a variable match using a terminal node of the concept node identified at block 608. The terminal node can define how to match certain parts of a user input to a particular concept. In some examples, the terminal node can define one or more seed words that are likely associated with a particular type of concept. For example, a terminal node for a place concept node can include the seed word “near,” indicating that the portion of an utterance following the word “near” likely refers to a place concept. Similarly, a terminal node for a time concept node can include the seed word “during,” indicating that the portion of an utterance following the word “during” likely refers to a time concept. When used to process the user input “what's the weather like near my brother's house during the Super Bowl?”, the terminal node for the place concept node can be used to identify “my brother's house” as a substring of the user input that likely refers to a place concept, and the terminal node for the time concept node can be used to identify “the Super Bowl” as a substring of the user input that likely refers to a time concept. It should be appreciated that identifying a substring of a user input that likely refers to a concept type can include identifying multiple potential substrings of the user input that could refer to the concept and selecting the potential substring having the highest confidence score as the substring of the user input that likely refers to the concept type.

In some examples, more than one type of concept can be identified at block 608. In these examples, block 610 can further include identifying substrings from the textual representation of user speech that correspond to those types of concepts. The terminal nodes of the concept nodes associated with those types of concepts can be used to identify the substrings by performing, for example, a variable match process using seed words.

At block 612, a second pass of the multi-pass natural language process can be performed to determine a secondary user intent for the first substring identified at block 610. In some examples, the secondary user intent for the unparsed first substring can be determined in a manner similar or identical to that used to determine the primary user intent for the textual representation of user speech at block 606. For example, the first substring can be treated as an input to the virtual assistant, and the virtual assistant can analyze, using processing modules 118 and the various components of virtual assistant 300 shown in FIG. 3, the first substring to identify possible parse results for the textual representation of user speech in a manner similar or identical to that described above with respect to block 606. Determining the secondary user intent can further include determining a confidence score for each of the alternative parse results representing the likelihood that a particular parse result is the correct parse result to apply to the first substring. The secondary user intent can be determined based on the parse result having the highest confidence score. For example, the secondary user intent can be determined to include the nodes associated with vocabulary, entities, concepts, properties, task flows that can be performed, dialog flows that can be performed, services that can be invoked, relationships between any of the forgoing, or the like, of the knowledge representation of user intent associated with the parse result having the highest confidence score.

In some examples, to reduce the amount of processing required, determining the secondary user intent at block 610 can include considering only possible parse results from domains that can potentially output that type of concept. For example, if determining the secondary user intent for a first substring that corresponds to a place concept, possible parse results or interpretations from the email domain can be excluded from consideration since the email domain may not output a place concept.

In some examples, more than one type of concept can be identified at block 608 and more than one substring can be identified from the textual representation of user speech at block 610 that correspond to these concepts. In these examples, block 612 can further include determining a secondary user intent for the additional substrings. For example, the additional substrings can be input into the virtual assistant and analyzed, using processing modules 118 and the various components of virtual assistant 300 shown in FIG. 3, to identify possible parse results for the substrings in a manner similar or identical to that of the first substring. Determining the secondary user intent can further include determining a confidence score for each of the alternative parse results representing the likelihood that a particular parse result is the correct parse result to apply to the additional substrings. The secondary user intent for the additional substrings can be determined based on the respective parse result having the highest confidence score.

In some examples, a concept identified at block 608 can include a sub-concept. In these examples, blocks 608, 610, and 612 can be recursively performed to identify the sub-concept from the secondary user intent, identify a substring of the substring corresponding to the concept, and determine a tertiary user intent for the substring of the substring corresponding to the concept. This recursive performance of blocks 608, 610, and 612 can be performed any number of times to perform additional passes of the multi-pass natural language process to process the concepts and sub-concepts of the user speech. The user intent determined for a lower level recursive pass can be provided to the user intent of a higher level recursive pass.

Once all passes of the multi-pass natural language process is complete, a task flow planning and execution process represented by block 614 can then be performed. At block 614, a task flow generated based on the primary user intent determined at block 606 and the secondary user intent 612 can be performed (and any subsequent passes of the multi-pass natural language process). In some examples, performing the task flow can include receiving the knowledge representation of user intent produced by the multi-pass natural language process and identifying a primary task flow to accomplish the primary user intent. The primary task flow can include a task flow identified by the primary user intent structure. For example, the primary task flow for a primary user intent structure representing a user intent to search for weather at a particular place and time can include performing a search query in an external weather service for the weather at the particular place and time.

Performing the task flow can further include identifying one or more constraints associated with the primary task flow. The one or more constraints can include any type of constraint imposed by the task flow, such as a type of input required by the task flow or a service required by the task flow.

Performing the task flow can further include identifying one or more queries, programs, methods, services, or APIs that can be performed to satisfy the one or more constraints. For example, the one or more queries, programs, methods, services, or APIs can be identified based on their ability to provide the type of input required by the primary task flow.

The tasks and order of the tasks to be performed in the task flow can be generated based on the primary task flow, the one or more constraints of the primary task flow, the identified one or more queries, programs, methods, services, or APIs, and the knowledge representation of user intent produced by the multi-pass natural language process. For example, based on the knowledge representation of user intent produced by the multi-pass natural language process, the domains that are to be used to generate parse results, the service methods required, and constraints of those services can be known. Given this information, an appropriate ordering of tasks can be generated to obtain the inputs required by tasks associated with the lowest level user intent structure (e.g., the tertiary user intent) to generate the required inputs for tasks associated with the higher level user intent structures (e.g., the secondary and primary user intents).

To illustrate the operation of process 600, one example audio input that can be received at block 602 can include the user speech “What is the weather like near the hockey game tonight?”. At block 604, the user speech can be converted into a textual representation of user speech. At block 606, the textual representation of user speech can be analyzed using, for example, the various components of virtual assistant 300 shown in FIG. 3 having active ontology 500 shown in FIG. 5, to identify possible parse results for the textual representation of user speech. In this example, each of weather domain 402, sports domain 418, reminder domain 420, business domain 422, and email domain 424 can generate possible parse results. A confidence score can be generated for the possible parse results and it can be determined that a parse result from weather domain 402 has the highest confidence score. This parse result can be used to determine that the likely user intent of the textual representation is that the user desires weather information.

At block 608, the user intent structure associated with the identified parse result can be searched for concept nodes to identify concepts referred to by the user intent. As shown in FIG. 5, the weather user intent structure can include time concept node 502 and place concept node 506. Thus, the semantic tags of these concept nodes can be read to identify and store the time concept and place concept represented by these concept nodes. At block 610, a substring corresponding to each of the types of concepts identified at block 608 can be identified. For example, terminal node 508 can be used to perform a variable match process on the textual representation “What is the weather like near the hockey game tonight?” using the seed word “near” to identify the substring “the hockey game tonight” as likely referring to the place concept. Terminal node 504 can be used to perform a variable match process on the textual representation “What is the weather like near the hockey game tonight?” using the seed word “during” to identify a substring that likely refers to the time concept. Since the textual representation of user speech does not include the seed word “during,” other rules associated with terminal node 504 can optionally be used to determine that “tonight” or “the hockey game tonight” may refer to the time concept, but may do so with a lower confidence.

At block 612, a secondary user intent can be determined for the substring identified at block 610. This can include analyzing using, for example, the various components of virtual assistant 300 shown in FIG. 3 having active ontology 500 shown in FIG. 5, to identify possible parse results for the substring. For example, the substring “the hockey game tonight” generated by terminal node 508 of place concept node 506 may have been the most confident weather result at block 610. Thus, each of weather domain 402, sports domain 418, reminder domain 420, business domain 422, and email domain 424 can generate possible parse results. However, in some examples, parse results from email domain 424 can be excluded since that domain does not output the place type concept. A confidence score can be generated for the possible parse results and it can be determined that a parse result of a sporting event from sports domain 418 has the highest confidence score. Since a sporting event has both a concept of a time and a place, the determined user intent satisfies both the place concept requirement of place concept node 506 and time concept node 502 of weather domain 402. Thus, the user intent of the sporting event can be returned to the weather domain user intent for the textual representation “What is the weather like near the hockey game tonight?” for both time concept node 502 and place concept node 506.

At block 614, task flow planning and execution can be performed. In some examples, this can include receiving the output of the multi-pass natural language process, performing service pipelining to unravel the multi-pass natural language output to take an output of one service and feed it into the input of another, and perform constraint validation and resolution.

For example, continuing with the example the textual representation “What is the weather like near the hockey game tonight?”, block 614 can include performing a query for the hockey game within the sports domain based on the output from the multi-pass natural language process and receiving the results. Since the multi-pass natural language process also indicates that the sports domain 418 output feeds into the input of the weather domain 402, constraints for weather domain 402 can be inspected. In this example, it can be determined that the constraints for weather domain 402 include the concepts of place and time, as well as a constraint that a latitude and longitude must be defined for the place.

Next, the output from the sports domain 418 can be evaluated to determine if it is a valid input for weather domain 402. For example, if multiple sporting events are output by weather domain 402, block 614 can include disambiguating the events to identify a most likely sporting event. In another example, if no sporting events are output by sports domain 418, a response should be presented to the user. In yet another example, if a sporting event is output by sports domain 418, then block 614 can include extracting the concept. In this example, the place concept can be extracted from the event. If the output of sports domain 418 is not valid, block 614 can include an attempt to resolve the error. In the event that there is no time concept, then block 614 can include extracting the time concept from the event or using a default value. In the event that there is a missing latitude or longitude value, block 614 can include identifying a service that has specified that it can resolve latitude and longitude for any place concept. The constraint and validation process can be applied to the additional identified services. For example, when passing the place to be resolved into latitude and longitude, the service can be validated and the cycle of validation and resolution can continue until a final result is generated.

Once the service parameters are fully resolved, the final service can be invoked. For example, once the parameters for the weather service are resolved, the service can be invoked using those parameters to produce a final result for the user. However, if that service feeds into another service, the planning and execution process described above can be repeated.

Using process 600, domains within an active ontology of a virtual assistant can advantageously refer to concepts shared between domains without having to specifically refer to every other domain within the active ontology that can produce that concept. Additionally, the domains do not require logic for calling the referenced domains, receiving the knowledge representations of user intent generated by those domains, identifying the knowledge representations of user intent that is most likely to provide the appropriate concept, and extracting the relevant concept from that knowledge representation of user intent. This reduces the time and effort required to add or modify domains within the active ontology.

While process 600 is described above for processing a spoken user input, it should be appreciated that it can similarly be used to process a text user input. For example, to process a text user input, blocks 602 and 604 may not be performed. Instead, the text user input can be received or accessed and blocks 606, 608, 610, 612, and 614 can be performed, as described above, on the text user input.

Additionally, it should be appreciated that the blocks of process 600 can be performed on user device 102, server system 110, or a combination of user device 102 and server system 110. For instance, in some examples, all blocks of process 600 can be performed on user device 102. In other examples, all blocks of process 600 can be performed at server system 110. In yet other examples, some blocks of process 600 can be performed at user device 102, while other blocks of process 600 can be performed at server system 110.

Electronic Device

In accordance with some examples, FIG. 7 shows a functional block diagram of an electronic device 700 configured in accordance with the principles of the various described examples. The functional blocks of the device can be implemented by hardware, software, or a combination of hardware and software to carry out the principles of the various described examples. It is understood by persons of skill in the art that the functional blocks described in FIG. 7 can be combined or separated into sub-blocks to implement the principles of the various described examples. Therefore, the description herein optionally supports any possible combination or separation or further definition of the functional blocks described herein.

As shown in FIG. 7, electronic device 700 can include a touch screen display unit 702 configured to display a user interface and to receive touch input, and a sound receiving unit 704 configured to receive sound input. In some examples, electronic device 700 can include a speaker unit 706 configured to generate sound. Electronic device 700 can further include a processing unit 708 coupled to touch screen display unit 702 and sound receiving unit 704 (and, optionally, coupled to speaker unit 706). In some examples, processing unit 708 can include a text receiving unit 710, a primary user intent determining unit 712, a concept identifying unit 714, a first substring identifying unit 716, a secondary user intent determining unit 718, a task flow performing unit 720, a second substring identifying unit 722, a third substring identifying unit 724, and a tertiary user intent determining unit 726.

Processing unit 708 can be configured to receive an audio input (e.g., from audio receiving unit 704) containing user speech. Processing unit 708 can be configured to perform speech-to-text conversion on the audio input to generate a textual representation of user speech. The textual representation of user speech can be received by text receiving unit 710. A primary user intent can be determined from the textual representation of user speech (e.g., using primary user intent determining unit 712). A first type of concept referred to by the primary user intent can be identified (e.g., using concept identifying unit 714). A first substring corresponding to the first type of concept can be identified from the textual representation of user speech (e.g., using first substring identifying unit 716). A secondary user intent for the first substring can be determined (e.g., using secondary user intent determining unit 718). A task flow comprising one or more tasks based at least in part on the primary user intent for the textual representation of user speech and the secondary user intent for the first substring can be performed (e.g., using task flow performing unit 720).

In some examples, processing unit 708 can be configured to determine the primary user intent for the textual representation of user speech (e.g., using primary user intent determining unit 712) by determining a confidence score for a plurality of interpretations of the textual representation of user speech and determining the primary user intent for the textual representation of user speech based on an interpretation of the plurality of interpretations of the textual representation of user speech having the highest confidence score. In some examples, the first type of concept comprises a place, a time, an event, or a person.

In some examples, processing unit 708 can be configured to identify the first substring from the textual representation of user speech (e.g., using first substring identifying unit 716) by identifying one or more predetermined words corresponding to the first type of concept in the textual representation of user speech and identifying the first substring based on the one or more predetermined words corresponding to the first type of concept.

In some examples, processing unit 708 can be configured to determine the secondary user intent for the first substring (e.g., using secondary user intent determining unit 718) by determining a confidence score for a plurality of interpretations of the first substring and determining the secondary user intent for the first substring based on an interpretation of the plurality of interpretations of the first substring having the highest confidence score. In some examples, the plurality of interpretations of the first substring can exclude interpretations from domains that do not output the first type of concept.

In some examples, processing unit 708 can be configured to identify a second type of concept referred to by the primary user intent (e.g., using concept identifying unit 714), identify a second substring from the textual representation of user speech corresponding to the second type of concept (e.g., using second substring identifying unit 722), and determine a secondary user intent for the second substring (e.g., using secondary user intent determining unit 718), wherein performing the task flow is further based on the secondary user intent for the second substring. In some examples, the second type of concept comprises a place, a time, an event, or a person.

In some examples, processing unit 708 can be configured to identify the second substring from the textual representation of user speech (e.g., using second substring identifying unit 722) by identifying one or more predetermined words corresponding to the second type of concept in the textual representation of user speech and identifying the second substring based on the one or more predetermined words corresponding to the second type of concept.

In some examples, processing unit 708 can be configured to determine the secondary user intent for the second substring (e.g., using secondary user intent determining unit 718) by determining a confidence score for a plurality of interpretations of the second substring and determining the secondary user intent for the second substring based on an interpretation of the plurality of interpretations of the second substring having the highest confidence score. In some examples, the plurality of interpretations of the second substring can exclude interpretations from domains that do not output the second type of concept.

In some examples, processing unit 708 can be configured to identify a third type of concept referred to by the secondary user intent for the first substring (e.g., using concept identifying unit 714), identify a third sub string from the first substring corresponding to the third type of concept (e.g., using third substring identifying unit 724), and determine a tertiary user intent for the third substring (e.g., using tertiary user intent determining unit 726), wherein performing the task flow is further based on the tertiary user intent for the third substring. In some examples, the third type of concept comprises a place, a time, an event, or a person.

In some examples, processing unit 708 can be configured to identify the third substring from the first substring (e.g., using third substring identifying unit 724) by identifying one or more predetermined words corresponding to the third type of concept in the first substring and identifying the third substring based on the one or more predetermined words corresponding to the third type of concept.

In some examples, processing unit 708 can be configured to determine the tertiary user intent for the third substring (e.g., using tertiary user intent determining unit 726) by determining a confidence score for a plurality of interpretations of the third substring and determining the tertiary user intent for the third substring based on an interpretation of the plurality of interpretations of the third substring having the highest confidence score. In some examples, the plurality of interpretations of the third substring can exclude interpretations from domains that do not output the third type of concept.

In some examples, processing unit 708 can be configured to perform the task flow (e.g., using task flow performing unit 720) by identifying a primary task flow to accomplish the primary user intent, identify one or more constraints associated with the primary task flow, identify one or more queries, programs, methods, services, or APIs that satisfy the one or more constraints associated with the primary task flow, and generate the task flow from the primary task flow and the identified one or more queries, programs, methods, services, or APIs. In some examples, the one or more constraints can include a type of input required by the primary task flow, and the identified one or more queries, programs, methods, services, or APIs can be capable of providing the type of input required by the primary task flow.

As described above, one aspect of the present technology is the gathering and use of data available from various sources to improve the delivery to users of invitational content or any other content that may be of interest to them. The present disclosure contemplates that in some instances, this gathered data can include personal information data that uniquely identifies or can be used to contact or locate a specific person. Such personal information data can include demographic data, location-based data, telephone numbers, email addresses, home addresses, or any other identifying information.

The present disclosure recognizes that the use of such personal information data, in the present technology, can be used to the benefit of users. For example, the personal information data can be used to deliver targeted content that is of greater interest to the user. Accordingly, use of such personal information data enables calculated control of the delivered content. Further, other uses for personal information data that benefit the user are also contemplated by the present disclosure.

The present disclosure further contemplates that the entities responsible for the collection, analysis, disclosure, transfer, storage, or other use of such personal information data will comply with well-established privacy policies and/or privacy practices. In particular, such entities should implement and consistently use privacy policies and practices that are generally recognized as meeting or exceeding industry or governmental requirements for maintaining personal information data private and secure. For example, personal information from users should be collected for legitimate and reasonable uses of the entity and not shared or sold outside of those legitimate uses. Further, such collection should occur only after receiving the informed consent of the users. Additionally, such entities would take any needed steps for safeguarding and securing access to such personal information data and ensuring that others with access to the personal information data adhere to their privacy policies and procedures. Further, such entities can subject themselves to evaluation by third parties to certify their adherence to widely accepted privacy policies and practices.

Despite the foregoing, the present disclosure also contemplates examples in which users selectively block the use of, or access to, personal information data. That is, the present disclosure contemplates that hardware and/or software elements can be provided to prevent or block access to such personal information data. For example, in the case of advertisement delivery services, the present technology can be configured to allow users to select to “opt in” or “opt out” of participation in the collection of personal information data during registration for services. In another example, users can select not to provide location information for targeted content delivery services. In yet another example, users can select to not provide precise location information, but permit the transfer of location zone information.

Therefore, although the present disclosure broadly covers use of personal information data to implement one or more various disclosed examples, the present disclosure also contemplates that the various examples can also be implemented without the need for accessing such personal information data. That is, the various examples of the present technology are not rendered inoperable due to the lack of all or a portion of such personal information data. For example, content can be selected and delivered to users by inferring preferences based on non-personal information data or a bare minimum amount of personal information, such as the content being requested by the device associated with a user, other non-personal information available to the content delivery services, or publicly available information.

Although examples have been fully described with reference to the accompanying drawings, it is to be noted that various changes and modifications will become apparent to those skilled in the art. Such changes and modifications are to be understood as being included within the scope of the various examples as defined by the appended claims. 

What is claimed is:
 1. A method for operating a digital assistant, the method comprising: at an electronic device having one or more processors and memory: receiving user speech input; generating a textual representation of the user speech input; parsing the textual representation to determine a primary domain representing a user intent for the textual representation; identifying a first substring from the textual representation that corresponds to a first attribute of the primary domain; parsing the identified first substring to determine a secondary domain representing a user intent for the first substring; and performing a task flow comprising one or more tasks based on the primary domain and the secondary domain.
 2. The method of claim 1, wherein parsing the textual representation comprises: determining a confidence score for a plurality of interpretations of the textual representation; and determining the primary domain representing the user intent for the textual representation based on an interpretation of the plurality of interpretations of the textual representation having the highest confidence score.
 3. The method of claim 1, wherein the first attribute comprises a place, a time, an event, or a person.
 4. The method of claim 1, wherein identifying the first substring from the textual representation comprises: identifying, in the textual representation, one or more predetermined words corresponding to the first attribute; and identifying the first substring based on the one or more predetermined words corresponding to the first attribute.
 5. The method of claim 1, wherein parsing the identified first substring comprises: determining a confidence score for a plurality of interpretations of the first substring; and determining the secondary domain representing the user intent for the first substring based on an interpretation of the plurality of interpretations of the first substring having the highest confidence score.
 6. The method of claim 5, wherein the plurality of interpretations of the first substring exclude interpretations from domains that do not include the first attribute.
 7. The method of claim 1, further comprising: identifying a second substring from the textual representation that corresponds to a second attribute of the primary domain; and parsing the identified second substring to determine a second secondary domain representing a user intent for the second substring, wherein performing the task flow is further based on the second secondary domain.
 8. The method of claim 7, wherein the second attribute comprises a place, a time, an event, or a person.
 9. The method of claim 7, wherein identifying the second substring from the textual representation comprises: identifying in the textual representation one or more predetermined words corresponding to the second attribute; and identifying the second substring based on the one or more predetermined words corresponding to the second attribute.
 10. The method of claim 7, wherein parsing the identified second substring comprises: determining a confidence score for a plurality of interpretations of the second substring; and determining the second secondary domain representing a user intent for the second substring based on an interpretation of the plurality of interpretations of the second substring having the highest confidence score.
 11. The method of claim 10, wherein the plurality of interpretations of the second substring exclude interpretations from domains that do not output the second attribute.
 12. The method of claim 1, wherein performing the task flow comprises: identifying a primary task flow to accomplish the user intent for the textual representation; identifying one or more constraints associated with the primary task flow; identifying one or more queries, programs, methods, services, or APIs that satisfy the one or more constraints associated with the primary task flow; and generating the task flow from the primary task flow and the identified one or more queries, programs, methods, services, or APIs.
 13. The method of claim 12, wherein the one or more constraints comprises a type of input required by the primary task flow, and wherein the identified one or more queries, programs, methods, services, or APIs are capable of providing the type of input required by the primary task flow.
 14. The method of claim 1, wherein the first attribute is represented by a first node of the primary domain, and wherein the first node of the primary domain stems from a root node of the primary domain.
 15. The method of claim 14, wherein the secondary domain includes a first node representing the first attribute, and wherein the first node of the secondary domain stems from a root node of the secondary domain.
 16. The method of claim 1, wherein the identified first substring is parsed to determine the secondary domain without parsing any portion of the text representation other than the identified first substring.
 17. The method of claim 1, wherein the secondary domain representing the user intent for the first substring is determined based on the first attribute.
 18. The method of claim 1, further comprising: determining a value for the first attribute based on the secondary domain, wherein the task flow is performed using the determined value for the first attribute.
 19. An electronic device, comprising: one or more processors; and memory storing one or more programs configured to be executed by the one or more processors, the one or more programs including instructions for: receiving user speech input; generating a textual representation of the user speech input; parsing the textual representation to determine a primary domain representing a user intent for the textual representation; identifying a first substring from the textual representation that corresponds to a first attribute of the primary domain; parsing the identified first substring to determine a secondary domain representing a user intent for the first substring; and performing a task flow comprising one or more tasks based on the primary domain and the secondary domain.
 20. A non-transitory computer-readable storage medium comprising computer-executable instructions for: receiving user speech input; generating a textual representation of the user speech input; parsing the textual representation to determine a primary domain representing a user intent for the textual representation; identifying a first substring from the textual representation that corresponds to a first attribute of the primary domain; parsing the identified first substring to determine a secondary domain representing a user intent for the first substring; and performing a task flow comprising one or more tasks based on the primary domain and the secondary domain. 